The other Apache Technologies your Big Data solution needsgagravarr
The document discusses many Apache projects relevant to big data solutions, including projects for loading and querying data like Pig and Gora, building MapReduce jobs like Avro and Thrift, cloud computing with LibCloud and DeltaCloud, and extracting information from unstructured data with Tika, UIMA, OpenNLP, and cTakes. It also mentions utility projects like Chemistry, JMeter, Commons, and ManifoldCF.
This document discusses SQL engines for Hadoop, including Hive, Presto, and Impala. Hive is best for batch jobs due to its stability. Presto provides interactive queries across data sources and is easier to manage than Hive with Tez. Presto's distributed architecture allows queries to run in parallel across nodes. It supports pluggable connectors to access different data stores and has language bindings for multiple clients.
Introduction to Apache Amaterasu (Incubating): CD Framework For Your Big Data...DataWorks Summit
In the last few years, the DevOps movement has introduced ground breaking approaches to the way we manage the lifecycle of software development and deployment. Today organisations aspire to fully automate the deployment of microservices and web applications with tools such as Chef, Puppet and Ansible. However, the deployment of data-processing pipelines remains a relic from the dark-ages of software development.
Processing large-scale data pipelines is the main engineering task of the Big Data era, and it should be treated with the same respect and craftsmanship as any other piece of software. That is why we created Apache Amaterasu (Incubating) - an open source framework that takes care of the specific needs of Big Data applications in the world of continuous delivery.
In this session, we will take a close look at Apache Amaterasu (Incubating) a simple and powerful framework to build and dispense pipelines. Amaterasu aims to help data engineers and data scientists to compose, configure, test, package, deploy and execute data pipelines written using multiple tools, languages and frameworks.
We will see what Amaterasu provides today, and how it can help existing Big Data application and demo some of the new bits that are coming in the near future.
Speaker:
Yaniv Rodenski, Senior Solutions Architect, Couchbase
Spark Meetup Amsterdam - Dealing with Bad Actors in ETL, DatabricksGoDataDriven
Stable and robust data pipelines are a critical component of the data infrastructure of enterprises. Most commonly, data pipelines ingest messy data sources with incorrect, incomplete or inconsistent records and produce curated and/or summarized data for consumption by subsequent applications.
In this talk we go over new and upcoming features in Spark that enable it to better serve such workloads. Such features include isolation of corrupt input records and files, useful diagnostic feedback to users and improved support for nested type handling which is common in ETL jobs.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
This document provides an overview and comparison of different data transformation frameworks including Apache Pig, Apache Hive, and Apache Spark. It discusses features such as file formats, source to target mappings, data quality checks, and core processing functionality. The document contains code examples demonstrating how to perform common ETL tasks in each framework using delimited, XML, JSON, and other file formats. It also covers topics like numeric validation, data mapping, and performance. The overall purpose is to help users understand the different options for large-scale data processing in Hadoop.
SQL on Hadoop: Defining the New Generation of Analytic SQL DatabasesOReillyStrata
The document summarizes Carl Steinbach's presentation on SQL on Hadoop. It discusses how earlier systems like Hive had limitations for analytics workloads due to using MapReduce. A new architecture runs PostgreSQL on worker nodes co-located with HDFS data to enable push-down query processing for better performance. Citus Data's CitusDB product was presented as an example of this architecture, allowing SQL queries to efficiently analyze petabytes of data stored in HDFS.
Spark SQL provides relational data processing capabilities in Spark. It introduces a DataFrame API that allows both relational operations on external data sources and Spark's built-in distributed collections. The Catalyst optimizer improves performance by applying database query optimization techniques. It is highly extensible, making it easy to add data sources, optimization rules, and data types for domains like machine learning. Spark SQL evaluation shows it outperforms alternative systems on both SQL query processing and Spark program workloads involving large datasets.
The document discusses functional programming concepts and their application to big data problems. It provides an overview of functional programming foundations and languages. Key functional programming concepts discussed include first-class functions, pure functions, recursion, and immutability. These concepts are well-suited for data-centric applications like Hadoop MapReduce. The document also presents a case study comparing an imperative approach to a transaction processing problem to a functional approach, showing that the functional version was faster and avoided side effects.
The other Apache Technologies your Big Data solution needsgagravarr
The document discusses many Apache projects relevant to big data solutions, including projects for loading and querying data like Pig and Gora, building MapReduce jobs like Avro and Thrift, cloud computing with LibCloud and DeltaCloud, and extracting information from unstructured data with Tika, UIMA, OpenNLP, and cTakes. It also mentions utility projects like Chemistry, JMeter, Commons, and ManifoldCF.
This document discusses SQL engines for Hadoop, including Hive, Presto, and Impala. Hive is best for batch jobs due to its stability. Presto provides interactive queries across data sources and is easier to manage than Hive with Tez. Presto's distributed architecture allows queries to run in parallel across nodes. It supports pluggable connectors to access different data stores and has language bindings for multiple clients.
Introduction to Apache Amaterasu (Incubating): CD Framework For Your Big Data...DataWorks Summit
In the last few years, the DevOps movement has introduced ground breaking approaches to the way we manage the lifecycle of software development and deployment. Today organisations aspire to fully automate the deployment of microservices and web applications with tools such as Chef, Puppet and Ansible. However, the deployment of data-processing pipelines remains a relic from the dark-ages of software development.
Processing large-scale data pipelines is the main engineering task of the Big Data era, and it should be treated with the same respect and craftsmanship as any other piece of software. That is why we created Apache Amaterasu (Incubating) - an open source framework that takes care of the specific needs of Big Data applications in the world of continuous delivery.
In this session, we will take a close look at Apache Amaterasu (Incubating) a simple and powerful framework to build and dispense pipelines. Amaterasu aims to help data engineers and data scientists to compose, configure, test, package, deploy and execute data pipelines written using multiple tools, languages and frameworks.
We will see what Amaterasu provides today, and how it can help existing Big Data application and demo some of the new bits that are coming in the near future.
Speaker:
Yaniv Rodenski, Senior Solutions Architect, Couchbase
Spark Meetup Amsterdam - Dealing with Bad Actors in ETL, DatabricksGoDataDriven
Stable and robust data pipelines are a critical component of the data infrastructure of enterprises. Most commonly, data pipelines ingest messy data sources with incorrect, incomplete or inconsistent records and produce curated and/or summarized data for consumption by subsequent applications.
In this talk we go over new and upcoming features in Spark that enable it to better serve such workloads. Such features include isolation of corrupt input records and files, useful diagnostic feedback to users and improved support for nested type handling which is common in ETL jobs.
Transformation Processing Smackdown; Spark vs Hive vs PigLester Martin
This document provides an overview and comparison of different data transformation frameworks including Apache Pig, Apache Hive, and Apache Spark. It discusses features such as file formats, source to target mappings, data quality checks, and core processing functionality. The document contains code examples demonstrating how to perform common ETL tasks in each framework using delimited, XML, JSON, and other file formats. It also covers topics like numeric validation, data mapping, and performance. The overall purpose is to help users understand the different options for large-scale data processing in Hadoop.
SQL on Hadoop: Defining the New Generation of Analytic SQL DatabasesOReillyStrata
The document summarizes Carl Steinbach's presentation on SQL on Hadoop. It discusses how earlier systems like Hive had limitations for analytics workloads due to using MapReduce. A new architecture runs PostgreSQL on worker nodes co-located with HDFS data to enable push-down query processing for better performance. Citus Data's CitusDB product was presented as an example of this architecture, allowing SQL queries to efficiently analyze petabytes of data stored in HDFS.
Spark SQL provides relational data processing capabilities in Spark. It introduces a DataFrame API that allows both relational operations on external data sources and Spark's built-in distributed collections. The Catalyst optimizer improves performance by applying database query optimization techniques. It is highly extensible, making it easy to add data sources, optimization rules, and data types for domains like machine learning. Spark SQL evaluation shows it outperforms alternative systems on both SQL query processing and Spark program workloads involving large datasets.
The document discusses functional programming concepts and their application to big data problems. It provides an overview of functional programming foundations and languages. Key functional programming concepts discussed include first-class functions, pure functions, recursion, and immutability. These concepts are well-suited for data-centric applications like Hadoop MapReduce. The document also presents a case study comparing an imperative approach to a transaction processing problem to a functional approach, showing that the functional version was faster and avoided side effects.
Spark SQL is a module for structured data processing in Spark. It provides DataFrames and the ability to execute SQL queries. Some key points:
- Spark SQL allows querying structured data using SQL, or via DataFrame/Dataset APIs for Scala, Java, Python, and R.
- It supports various data sources like Hive, Parquet, JSON, and more. Data can be loaded and queried using a unified interface.
- The SparkSession API combines SparkContext with SQL functionality and is used to create DataFrames from data sources, register databases/tables, and execute SQL queries.
Spark SQL is a component of Apache Spark that introduces SQL support. It includes a DataFrame API that allows users to write SQL queries on Spark, a Catalyst optimizer that converts logical queries to physical plans, and data source APIs that provide a unified way to read/write data in various formats. Spark SQL aims to make SQL queries on Spark more efficient and extensible.
This document discusses Apache Spark, an open-source cluster computing framework. It describes how Spark allows for faster iterative algorithms and interactive data mining by keeping working sets in memory. The document also provides an overview of Spark's ease of use in Scala and Python, built-in modules for SQL, streaming, machine learning, and graph processing, and compares Spark's machine learning library MLlib to other frameworks.
In this talk, we’ll discuss technical designs of support of HBase as a “native” data source to Spark SQL to achieve both query and load performance and scalability: near-precise execution locality of query and loading, fine-tuned partition pruning, predicate pushdown, plan execution through coprocessor, and optimized and fully parallelized bulk loader. Point and range queries on dimensional attributes will benefit particularly well from the techniques. Preliminary test results vs. established SQL-on-HBase technologies will be provided. The speaker will also share the future plan and real-world use cases, particularly in the telecom industry.
In this presentation, Vineet will be explaining case study of one of my customers using Spark to migrate terabytes of data from GPFS into Hive tables. The ETL pipeline was built purely using Spark. The pipeline extracted target (Hive) table properties such as - identification of Hive Date/Timestamp columns, whether target table is partitioned or non-partitioned, target storage formats (Parquet or Avro) and source to target columns mappings. These target tables contain few to hundreds of columns and non standard date fomats into Hive standard timestamp format.
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications. In this webinar, developers will learn:
*How Spark Streaming works - a quick review.
*Features in Spark Streaming that help prevent potential data loss.
*Complementary tools in a streaming pipeline - Kafka and Akka.
*Design and tuning tips for Reactive Spark Streaming applications.
The document discusses several big data frameworks: Spark, Presto, Cloudera Impala, and Apache Hadoop. Spark aims to make data analytics faster by loading data into memory for iterative querying. Presto extends R with distributed parallelism for scalable machine learning and graph algorithms. Hadoop uses MapReduce to distribute computations across large hardware clusters and handles failures automatically. While useful for batch processing, Hadoop has disadvantages for small files and online transactions.
The document provides an agenda and overview for a Big Data Warehousing meetup hosted by Caserta Concepts. The meetup agenda includes an introduction to SparkSQL with a deep dive on SparkSQL and a demo. Elliott Cordo from Caserta Concepts will provide an introduction and overview of Spark as well as a demo of SparkSQL. The meetup aims to share stories in the rapidly changing big data landscape and provide networking opportunities for data professionals.
How to use Parquet as a Sasis for ETL and AnalyticsDataWorks Summit
Parquet is a columnar storage format that provides efficient compression and querying capabilities. It aims to store data efficiently for analysis while supporting interoperability across systems. Parquet uses column-oriented storage with efficient encodings and statistics to enable fast querying of large datasets. It integrates with many query engines and frameworks like Hive, Impala, Spark and MapReduce to allow projection and predicate pushdown for optimized queries.
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginn...Simplilearn
This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document is a presentation about using Hadoop for analytic workloads. It discusses how Hadoop has traditionally been used for batch processing but can now also be used for interactive queries and business intelligence workloads using tools like Impala, Parquet, and HDFS. It summarizes performance tests showing Impala can outperform MapReduce for queries and scales linearly with additional nodes. The presentation argues Hadoop provides an effective solution for certain data warehouse workloads while maintaining flexibility, ease of scaling, and cost effectiveness.
Building large scale transactional data lake using apache hudiBill Liu
Data is a critical infrastructure for building machine learning systems. From ensuring accurate ETAs to predicting optimal traffic routes, providing safe, seamless transportation and delivery experiences on the Uber platform requires reliable, performant large-scale data storage and analysis. In 2016, Uber developed Apache Hudi, an incremental processing framework, to power business critical data pipelines at low latency and high efficiency, and helps distributed organizations build and manage petabyte-scale data lakes.
In this talk, I will describe what is APache Hudi and its architectural design, and then deep dive to improving data operations by providing features such as data versioning, time travel.
We will also go over how Hudi brings kappa architecture to big data systems and enables efficient incremental processing for near real time use cases.
Speaker: Satish Kotha (Uber)
Apache Hudi committer and Engineer at Uber. Previously, he worked on building real time distributed storage systems like Twitter MetricsDB and BlobStore.
website: https://www.aicamp.ai/event/eventdetails/W2021043010
Easy, Scalable, Fault-tolerant stream processing with Structured Streaming in...DataWorks Summit
Last year, in Apache Spark 2.0, we introduced Structured Steaming, a new stream processing engine built on Spark SQL, which revolutionized how developers could write stream processing application. Structured Streaming enables users to express their computations the same way they would express a batch query on static data. Developers can express queries using powerful high-level APIs including DataFrames, Dataset and SQL. Then, the Spark SQL engine is capable of converting these batch-like transformations into an incremental execution plan that can process streaming data, while automatically handling late, out-of-order data, and ensuring end-to-end exactly-once fault-tolerance guarantees.
Since Spark 2.0 we've been hard at work building first class integration with Kafka. With this new connectivity, performing complex, low-latency analytics is now as easy as writing a standard SQL query. This functionality in addition to the existing connectivity of Spark SQL make it easy to analyze data using one unified framework. Users can now seamlessly extract insights from data, independent of whether it is coming from messy / unstructured files, a structured / columnar historical data warehouse or arriving in real-time from pubsub systems like Kafka and Kinesis.
We'll walk through a concrete example where in less than 10 lines, we read Kafka, parse JSON payload data into separate columns, transform it, enrich it by joining with static data and write it out as a table ready for batch and ad-hoc queries on up-to-the-last-minute data. We'll use techniques including event-time based aggregations, arbitrary stateful operations, and automatic state management using event-time watermarks.
This document introduces Spark SQL and the Catalyst query optimizer. It discusses that Spark SQL allows executing SQL on Spark, builds SchemaRDDs, and optimizes query execution plans. It then provides details on how Catalyst works, including its use of logical expressions, operators, and rules to transform query trees and optimize queries. Finally, it outlines some interesting open issues and how to contribute to Spark SQL's development.
Introduction to Sqoop Aaron Kimball Cloudera Hadoop User Group UKSkills Matter
In this talk of Hadoop User Group UK meeting, Aaron Kimball from Cloudera introduces Sqoop, the open source SQL-to-Hadoop tool. Sqoop helps users perform efficient imports of data from RDBMS sources to Hadoop's distributed file system, where it can be processed in concert with other data sources. Sqoop also allows users to export Hadoop-generated results back to an RDBMS for use with other data pipelines.
After this session, users will understand how databases and Hadoop fit together, and how to use Sqoop to move data between these systems. The talk will provide suggestions for best practices when integrating Sqoop and Hadoop in your data processing pipelines. We'll also cover some deeper technical details of Sqoop's architecture, and take a look at some upcoming aspects of Sqoop's development roadmap.
SF Big Analytics 2020-07-28
Anecdotal history of Data Lake and various popular implementation framework. Why certain tradeoff was made to solve the problems, such as cloud storage, incremental processing, streaming and batch unification, mutable table, ...
Ingesting data at scale into elasticsearch with apache pulsarTimothy Spann
Ingesting data at scale into elasticsearch with apache pulsar
FLiP
Flink, Pulsar, Spark, NiFi, ElasticSearch, MQTT, JSON
data ingest
etl
elt
sql
timothy spann
elasticsearch community conference
2/11/2022
developer advocate at streamnative
This document provides an agenda for a presentation on using machine learning with Apache Spark. The presentation introduces Apache Spark and its architecture, Scala notebooks in Spark, machine learning components in Spark, pipelines for machine learning tasks, and examples of regression, classification, clustering, collaborative filtering, and model tuning in Spark. It aims to provide a hands-on, example-driven introduction to applying machine learning using Apache Spark without deep dives into Spark architecture or algorithms.
This document discusses layered architecture and repositories in domain-driven design. It covers topics like separating domain models from data models using repositories, implementing repositories to provide a collection-like interface for accessing domain objects, and differences between repositories and data access objects. The key points are that repositories mediate between the domain and data layers, encapsulate data access, and make the domain model independent of data access technologies and schemas.
MySQL 5.7 is GA. Here is the news about our NoSQL features in MySQL and MySQL Cluster, with a lot of emphasize on the new JSON features that make MySQL suitable as a document store.
Spark SQL is a module for structured data processing in Spark. It provides DataFrames and the ability to execute SQL queries. Some key points:
- Spark SQL allows querying structured data using SQL, or via DataFrame/Dataset APIs for Scala, Java, Python, and R.
- It supports various data sources like Hive, Parquet, JSON, and more. Data can be loaded and queried using a unified interface.
- The SparkSession API combines SparkContext with SQL functionality and is used to create DataFrames from data sources, register databases/tables, and execute SQL queries.
Spark SQL is a component of Apache Spark that introduces SQL support. It includes a DataFrame API that allows users to write SQL queries on Spark, a Catalyst optimizer that converts logical queries to physical plans, and data source APIs that provide a unified way to read/write data in various formats. Spark SQL aims to make SQL queries on Spark more efficient and extensible.
This document discusses Apache Spark, an open-source cluster computing framework. It describes how Spark allows for faster iterative algorithms and interactive data mining by keeping working sets in memory. The document also provides an overview of Spark's ease of use in Scala and Python, built-in modules for SQL, streaming, machine learning, and graph processing, and compares Spark's machine learning library MLlib to other frameworks.
In this talk, we’ll discuss technical designs of support of HBase as a “native” data source to Spark SQL to achieve both query and load performance and scalability: near-precise execution locality of query and loading, fine-tuned partition pruning, predicate pushdown, plan execution through coprocessor, and optimized and fully parallelized bulk loader. Point and range queries on dimensional attributes will benefit particularly well from the techniques. Preliminary test results vs. established SQL-on-HBase technologies will be provided. The speaker will also share the future plan and real-world use cases, particularly in the telecom industry.
In this presentation, Vineet will be explaining case study of one of my customers using Spark to migrate terabytes of data from GPFS into Hive tables. The ETL pipeline was built purely using Spark. The pipeline extracted target (Hive) table properties such as - identification of Hive Date/Timestamp columns, whether target table is partitioned or non-partitioned, target storage formats (Parquet or Avro) and source to target columns mappings. These target tables contain few to hundreds of columns and non standard date fomats into Hive standard timestamp format.
Spark Streaming makes it easy to build scalable fault-tolerant streaming applications. In this webinar, developers will learn:
*How Spark Streaming works - a quick review.
*Features in Spark Streaming that help prevent potential data loss.
*Complementary tools in a streaming pipeline - Kafka and Akka.
*Design and tuning tips for Reactive Spark Streaming applications.
The document discusses several big data frameworks: Spark, Presto, Cloudera Impala, and Apache Hadoop. Spark aims to make data analytics faster by loading data into memory for iterative querying. Presto extends R with distributed parallelism for scalable machine learning and graph algorithms. Hadoop uses MapReduce to distribute computations across large hardware clusters and handles failures automatically. While useful for batch processing, Hadoop has disadvantages for small files and online transactions.
The document provides an agenda and overview for a Big Data Warehousing meetup hosted by Caserta Concepts. The meetup agenda includes an introduction to SparkSQL with a deep dive on SparkSQL and a demo. Elliott Cordo from Caserta Concepts will provide an introduction and overview of Spark as well as a demo of SparkSQL. The meetup aims to share stories in the rapidly changing big data landscape and provide networking opportunities for data professionals.
How to use Parquet as a Sasis for ETL and AnalyticsDataWorks Summit
Parquet is a columnar storage format that provides efficient compression and querying capabilities. It aims to store data efficiently for analysis while supporting interoperability across systems. Parquet uses column-oriented storage with efficient encodings and statistics to enable fast querying of large datasets. It integrates with many query engines and frameworks like Hive, Impala, Spark and MapReduce to allow projection and predicate pushdown for optimized queries.
Spark SQL Tutorial | Spark SQL Using Scala | Apache Spark Tutorial For Beginn...Simplilearn
This presentation about Spark SQL will help you understand what is Spark SQL, Spark SQL features, architecture, data frame API, data source API, catalyst optimizer, running SQL queries and a demo on Spark SQL. Spark SQL is an Apache Spark's module for working with structured and semi-structured data. It is originated to overcome the limitations of Apache Hive. Now, let us get started and understand Spark SQL in detail.
Below topics are explained in this Spark SQL presentation:
1. What is Spark SQL?
2. Spark SQL features
3. Spark SQL architecture
4. Spark SQL - Dataframe API
5. Spark SQL - Data source API
6. Spark SQL - Catalyst optimizer
7. Running SQL queries
8. Spark SQL demo
This Apache Spark and Scala certification training is designed to advance your expertise working with the Big Data Hadoop Ecosystem. You will master essential skills of the Apache Spark open source framework and the Scala programming language, including Spark Streaming, Spark SQL, machine learning programming, GraphX programming, and Shell Scripting Spark. This Scala Certification course will give you vital skillsets and a competitive advantage for an exciting career as a Hadoop Developer.
What is this Big Data Hadoop training course about?
The Big Data Hadoop and Spark developer course have been designed to impart an in-depth knowledge of Big Data processing using Hadoop and Spark. The course is packed with real-life projects and case studies to be executed in the CloudLab.
What are the course objectives?
Simplilearn’s Apache Spark and Scala certification training are designed to:
1. Advance your expertise in the Big Data Hadoop Ecosystem
2. Help you master essential Apache and Spark skills, such as Spark Streaming, Spark SQL, machine learning programming, GraphX programming and Shell Scripting Spark
3. Help you land a Hadoop developer job requiring Apache Spark expertise by giving you a real-life industry project coupled with 30 demos
What skills will you learn?
By completing this Apache Spark and Scala course you will be able to:
1. Understand the limitations of MapReduce and the role of Spark in overcoming these limitations
2. Understand the fundamentals of the Scala programming language and its features
3. Explain and master the process of installing Spark as a standalone cluster
4. Develop expertise in using Resilient Distributed Datasets (RDD) for creating applications in Spark
5. Master Structured Query Language (SQL) using SparkSQL
6. Gain a thorough understanding of Spark streaming features
7. Master and describe the features of Spark ML programming and GraphX programming
Learn more at https://www.simplilearn.com/big-data-and-analytics/apache-spark-scala-certification-training
Big Data is a collection of large and complex data sets that cannot be processed using regular database management tools or processing applications. A lot of challenges such as capture, curation, storage, search, sharing, analysis, and visualization can be encountered while handling Big Data. On the other hand the Apache Hadoop software library is a framework that allows for the distributed processing of large data sets across clusters of computers using simple programming models. It is designed to scale up from single servers to thousands of machines, each offering local computation and storage. Big Data certification is one of the most recognized credentials of today.
For more details Click http://www.simplilearn.com/big-data-and-analytics/big-data-and-hadoop-training
The document is a presentation about using Hadoop for analytic workloads. It discusses how Hadoop has traditionally been used for batch processing but can now also be used for interactive queries and business intelligence workloads using tools like Impala, Parquet, and HDFS. It summarizes performance tests showing Impala can outperform MapReduce for queries and scales linearly with additional nodes. The presentation argues Hadoop provides an effective solution for certain data warehouse workloads while maintaining flexibility, ease of scaling, and cost effectiveness.
Building large scale transactional data lake using apache hudiBill Liu
Data is a critical infrastructure for building machine learning systems. From ensuring accurate ETAs to predicting optimal traffic routes, providing safe, seamless transportation and delivery experiences on the Uber platform requires reliable, performant large-scale data storage and analysis. In 2016, Uber developed Apache Hudi, an incremental processing framework, to power business critical data pipelines at low latency and high efficiency, and helps distributed organizations build and manage petabyte-scale data lakes.
In this talk, I will describe what is APache Hudi and its architectural design, and then deep dive to improving data operations by providing features such as data versioning, time travel.
We will also go over how Hudi brings kappa architecture to big data systems and enables efficient incremental processing for near real time use cases.
Speaker: Satish Kotha (Uber)
Apache Hudi committer and Engineer at Uber. Previously, he worked on building real time distributed storage systems like Twitter MetricsDB and BlobStore.
website: https://www.aicamp.ai/event/eventdetails/W2021043010
Easy, Scalable, Fault-tolerant stream processing with Structured Streaming in...DataWorks Summit
Last year, in Apache Spark 2.0, we introduced Structured Steaming, a new stream processing engine built on Spark SQL, which revolutionized how developers could write stream processing application. Structured Streaming enables users to express their computations the same way they would express a batch query on static data. Developers can express queries using powerful high-level APIs including DataFrames, Dataset and SQL. Then, the Spark SQL engine is capable of converting these batch-like transformations into an incremental execution plan that can process streaming data, while automatically handling late, out-of-order data, and ensuring end-to-end exactly-once fault-tolerance guarantees.
Since Spark 2.0 we've been hard at work building first class integration with Kafka. With this new connectivity, performing complex, low-latency analytics is now as easy as writing a standard SQL query. This functionality in addition to the existing connectivity of Spark SQL make it easy to analyze data using one unified framework. Users can now seamlessly extract insights from data, independent of whether it is coming from messy / unstructured files, a structured / columnar historical data warehouse or arriving in real-time from pubsub systems like Kafka and Kinesis.
We'll walk through a concrete example where in less than 10 lines, we read Kafka, parse JSON payload data into separate columns, transform it, enrich it by joining with static data and write it out as a table ready for batch and ad-hoc queries on up-to-the-last-minute data. We'll use techniques including event-time based aggregations, arbitrary stateful operations, and automatic state management using event-time watermarks.
This document introduces Spark SQL and the Catalyst query optimizer. It discusses that Spark SQL allows executing SQL on Spark, builds SchemaRDDs, and optimizes query execution plans. It then provides details on how Catalyst works, including its use of logical expressions, operators, and rules to transform query trees and optimize queries. Finally, it outlines some interesting open issues and how to contribute to Spark SQL's development.
Introduction to Sqoop Aaron Kimball Cloudera Hadoop User Group UKSkills Matter
In this talk of Hadoop User Group UK meeting, Aaron Kimball from Cloudera introduces Sqoop, the open source SQL-to-Hadoop tool. Sqoop helps users perform efficient imports of data from RDBMS sources to Hadoop's distributed file system, where it can be processed in concert with other data sources. Sqoop also allows users to export Hadoop-generated results back to an RDBMS for use with other data pipelines.
After this session, users will understand how databases and Hadoop fit together, and how to use Sqoop to move data between these systems. The talk will provide suggestions for best practices when integrating Sqoop and Hadoop in your data processing pipelines. We'll also cover some deeper technical details of Sqoop's architecture, and take a look at some upcoming aspects of Sqoop's development roadmap.
SF Big Analytics 2020-07-28
Anecdotal history of Data Lake and various popular implementation framework. Why certain tradeoff was made to solve the problems, such as cloud storage, incremental processing, streaming and batch unification, mutable table, ...
Ingesting data at scale into elasticsearch with apache pulsarTimothy Spann
Ingesting data at scale into elasticsearch with apache pulsar
FLiP
Flink, Pulsar, Spark, NiFi, ElasticSearch, MQTT, JSON
data ingest
etl
elt
sql
timothy spann
elasticsearch community conference
2/11/2022
developer advocate at streamnative
This document provides an agenda for a presentation on using machine learning with Apache Spark. The presentation introduces Apache Spark and its architecture, Scala notebooks in Spark, machine learning components in Spark, pipelines for machine learning tasks, and examples of regression, classification, clustering, collaborative filtering, and model tuning in Spark. It aims to provide a hands-on, example-driven introduction to applying machine learning using Apache Spark without deep dives into Spark architecture or algorithms.
This document discusses layered architecture and repositories in domain-driven design. It covers topics like separating domain models from data models using repositories, implementing repositories to provide a collection-like interface for accessing domain objects, and differences between repositories and data access objects. The key points are that repositories mediate between the domain and data layers, encapsulate data access, and make the domain model independent of data access technologies and schemas.
MySQL 5.7 is GA. Here is the news about our NoSQL features in MySQL and MySQL Cluster, with a lot of emphasize on the new JSON features that make MySQL suitable as a document store.
<b>Elevate MongoDB with ODBC/JDBC </b>[4:05 pm - 4:25 pm]<br />Adoption for MongoDB is growing across the enterprise and disrupting existing business intelligence, analytics and data integration infrastructure. Join us to disrupt that disruption using ODBC and JDBC access to MongoDB for instant out-of-box integration with existing infrastructure to elevate and expand your organization’s MongoDB footprint. We'll talk about common challenges and gotchas that shops face when exposing unstructured and semi-structured data using these established data connectivity standards. Existing infrastructure requirements should not dictate developers’ freedom of choice in a database
GDG Cloud Southlake #16: Priyanka Vergadia: Scalable Data Analytics in Google...James Anderson
Do you know The Cloud Girl? She makes the cloud come alive with pictures and storytelling.
The Cloud Girl, Priyanka Vergadia, Chief Content Officer @Google, joins us to tell us about Scaleable Data Analytics in Google Cloud.
Maybe, with her explanation, we'll finally understand it!
Priyanka is a technical storyteller and content creator who has created over 300 videos, articles, podcasts, courses and tutorials which help developers learn Google Cloud fundamentals, solve their business challenges and pass certifications! Checkout her content on Google Cloud Tech Youtube channel.
Priyanka enjoys drawing and painting which she tries to bring to her advocacy.
Check out her website The Cloud Girl: https://thecloudgirl.dev/ and her new book: https://www.amazon.com/Visualizing-Google-Cloud-Illustrated-References/dp/1119816327
FOSDEM 2015 - NoSQL and SQL the best of both worldsAndrew Morgan
This document discusses the benefits and limitations of both SQL and NoSQL databases. It argues that while NoSQL databases provide benefits like simple data formats and scalability, relying solely on them can result in data duplication and inconsistent data when relationships are not properly modeled. The document suggests that MySQL Cluster provides a hybrid approach, allowing both SQL queries and NoSQL interfaces while ensuring ACID compliance and referential integrity through its transactional capabilities and handling of foreign keys.
ER/Studio and DB PowerStudio Launch Webinar: Big Data, Big Models, Big News! Embarcadero Technologies
Watch the accompanying webinar presentation at http://embt.co/BigXE6
These are the slides for the ER/Studio and DB PowerStudio Launch Webinar: Big Data, Big Models, Big News! on September 18, 2014
Challenges of Implementing an Advanced SQL Engine on HadoopDataWorks Summit
Big SQL 3.0 is IBM's SQL engine for Hadoop that addresses challenges of building a first class SQL engine on Hadoop. It uses a modern MPP shared-nothing architecture and is architected from the ground up for low latency and high throughput. Key challenges included data placement on Hadoop, reading and writing Hadoop file formats, query optimization with limited statistics, and resource management with a shared Hadoop cluster. The architecture utilizes existing SQL query rewrite and optimization capabilities while introducing new capabilities for statistics, constraints, and pushdown to Hadoop file formats and data sources.
The outline of the presentation (presented at NDC 2011, Oslo, Norway):
- Short summary of OData evolution and current state
- Quick presentation of tools used to build and test OData services and clients (Visual Studio, LinqPad, Fiddler)
- Definition of canonical REST service, conformance of DataService-based implementation
- Updateable OData services
- Sharing single conceptual data model between databases from different vendors
- OData services without Entity Framework (NHibernate, custom data provider)
- Practical tips (logging, WCF binding, deployment)
Presto talk @ Global AI conference 2018 Bostonkbajda
Presented at Global AI Conference in Boston 2018:
http://www.globalbigdataconference.com/boston/global-artificial-intelligence-conference-106/speaker-details/kamil-bajda-pawlikowski-62952.html
Presto, an open source distributed SQL engine, is widely recognized for its low-latency queries, high concurrency, and native ability to query multiple data sources. Proven at scale in a variety of use cases at Facebook, Airbnb, Netflix, Uber, Twitter, LinkedIn, Bloomberg, and FINRA, Presto experienced an unprecedented growth in popularity in both on-premises and cloud deployments in the last few years. Presto is really a SQL-on-Anything engine in a single query can access data from Hadoop, S3-compatible object stores, RDBMS, NoSQL and custom data stores. This talk will cover some of the best use cases for Presto, recent advancements in the project such as Cost-Based Optimizer and Geospatial functions as well as discuss the roadmap going forward.
Tutorial Workgroup - Model versioning and collaborationPascalDesmarets1
Hackolade Studio has native integration with Git repositories to provide state-of-the-art collaboration, versioning, branching, conflict resolution, peer review workflows, change tracking and traceability. Mostly, it allows to co-locate data models and schemas with application code, and further integrate with DevOps CI/CD pipelines as part of our vision for Metadata-as-Code.
Co-located application code and data models provide the single source-of-truth for business and technical stakeholders.
This webinar (done in December,2007) shows how the new Data Services capability in WSO2's Web Services Application Server can become a key component in your SOA/Data strategy. Using simple screens and a basic knowledge of SQL, any database programmer or administrator can configure and expose Data Services. As well as major databases such as Oracle, DB2 and MySQL, you can also extract data from Excel and CSV files.
The document discusses Mondrian, an open source OLAP server written in Java. It can be used to develop a trajectory data warehouse and interactively analyze large datasets stored in SQL databases without writing SQL. Mondrian uses MDX and XML for querying and cube definition. It provides an OLAP view of relational data and enables fast, on-line analytical processing through aggregation and caching. GeoMondrian extends it with spatial/GIS data types and functions for geographical analysis.
From Legacy Database to Domain Layer Using a New Cincom VisualWorks ToolESUG
The document discusses updates and new features in GLORP (Generic Lightweight Object Relational Persistence), Cincom's object-relational mapping framework for Smalltalk. Key points include:
- Support for PostgreSQL 3.0 and ODBC 3.0 in the External Database Interface (EXDI).
- Improvements to GLORP including support for composite primary keys, nested iterators in queries, and exempting more literals from rollbacks.
- The mapping process between databases, GLORP modeling tools, and domain objects. A quick-start mapping tool called GlorpAtlasUI is demonstrated.
- Questions are invited about GLORP, EXDI, AppeX,
The document discusses upcoming changes and new features in MySQL 5.7. Key points include:
- MySQL 5.7 development has focused on performance, scalability, security and refactoring code.
- New features include online DDL support for additional DDL statements, InnoDB support for spatial data types, and cost information added to EXPLAIN output.
- Benchmarks show MySQL 5.7 providing significantly higher performance than previous versions, with a peak of 645,000 queries/second on some workloads.
MongoDB SoCal 2020: Migrate Anything* to MongoDB AtlasMongoDB
This presentation discusses migrating data from other data stores to MongoDB Atlas. It begins by explaining why MongoDB and Atlas are good choices for data management. Several preparation steps are covered, including sizing the target Atlas cluster, increasing the source oplog, and testing connectivity. Live migration, mongomirror, and dump/restore options are presented for migrating between replicasets or sharded clusters. Post-migration steps like monitoring and backups are also discussed. Finally, migrating from other data stores like AWS DocumentDB, Azure CosmosDB, DynamoDB, and relational databases are briefly covered.
A Pipeline for Distributed Topic and Sentiment Analysis of Tweets on Pivotal ...Srivatsan Ramanujam
Unstructured data is everywhere - in the form of posts, status updates, bloglets or news feeds in social media or in the form of customer interactions Call Center CRM. While many organizations study and monitor social media for tracking brand value and targeting specific customer segments, in our experience blending the unstructured data with the structured data in supplementing data science models has been far more effective than working with it independently.
In this talk we will show case an end-to-end topic and sentiment analysis pipeline we've built on the Pivotal Greenplum Database platform for Twitter feeds from GNIP, using open source tools like MADlib and PL/Python. We've used this pipeline to build regression models to predict commodity futures from tweets and in enhancing churn models for telecom through topic and sentiment analysis of call center transcripts. All of this was possible because of the flexibility and extensibility of the platform we worked with.
Gimel is a data abstraction framework built on Apache Spark - providing unified Data Access via API & SQL to different technologies such as kafka, elastic, HBASE, Rest API, File, Object stores, Relational , etc.
We spoke about this recently in the "cloud track" in the "Scale By The Bay" Conference.
https://www.scale.bythebay.io/schedule
https://sched.co/e55D
Youtube - https://www.youtube.com/watch?v=cy8g2WZbEBI&ab_channel=FunctionalTV
https://youtu.be/m6_0iI4XDpU
Similar to Apache MetaModel - unified access to all your data points (20)
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
How to Get CNIC Information System with Paksim Ga.pptxdanishmna97
Pakdata Cf is a groundbreaking system designed to streamline and facilitate access to CNIC information. This innovative platform leverages advanced technology to provide users with efficient and secure access to their CNIC details.
Threats to mobile devices are more prevalent and increasing in scope and complexity. Users of mobile devices desire to take full advantage of the features
available on those devices, but many of the features provide convenience and capability but sacrifice security. This best practices guide outlines steps the users can take to better protect personal devices and information.
HCL Notes and Domino License Cost Reduction in the World of DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-and-domino-license-cost-reduction-in-the-world-of-dlau/
The introduction of DLAU and the CCB & CCX licensing model caused quite a stir in the HCL community. As a Notes and Domino customer, you may have faced challenges with unexpected user counts and license costs. You probably have questions on how this new licensing approach works and how to benefit from it. Most importantly, you likely have budget constraints and want to save money where possible. Don’t worry, we can help with all of this!
We’ll show you how to fix common misconfigurations that cause higher-than-expected user counts, and how to identify accounts which you can deactivate to save money. There are also frequent patterns that can cause unnecessary cost, like using a person document instead of a mail-in for shared mailboxes. We’ll provide examples and solutions for those as well. And naturally we’ll explain the new licensing model.
Join HCL Ambassador Marc Thomas in this webinar with a special guest appearance from Franz Walder. It will give you the tools and know-how to stay on top of what is going on with Domino licensing. You will be able lower your cost through an optimized configuration and keep it low going forward.
These topics will be covered
- Reducing license cost by finding and fixing misconfigurations and superfluous accounts
- How do CCB and CCX licenses really work?
- Understanding the DLAU tool and how to best utilize it
- Tips for common problem areas, like team mailboxes, functional/test users, etc
- Practical examples and best practices to implement right away
Essentials of Automations: The Art of Triggers and Actions in FMESafe Software
In this second installment of our Essentials of Automations webinar series, we’ll explore the landscape of triggers and actions, guiding you through the nuances of authoring and adapting workspaces for seamless automations. Gain an understanding of the full spectrum of triggers and actions available in FME, empowering you to enhance your workspaces for efficient automation.
We’ll kick things off by showcasing the most commonly used event-based triggers, introducing you to various automation workflows like manual triggers, schedules, directory watchers, and more. Plus, see how these elements play out in real scenarios.
Whether you’re tweaking your current setup or building from the ground up, this session will arm you with the tools and insights needed to transform your FME usage into a powerhouse of productivity. Join us to discover effective strategies that simplify complex processes, enhancing your productivity and transforming your data management practices with FME. Let’s turn complexity into clarity and make your workspaces work wonders!
UiPath Test Automation using UiPath Test Suite series, part 6DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
In the rapidly evolving landscape of technologies, XML continues to play a vital role in structuring, storing, and transporting data across diverse systems. The recent advancements in artificial intelligence (AI) present new methodologies for enhancing XML development workflows, introducing efficiency, automation, and intelligent capabilities. This presentation will outline the scope and perspective of utilizing AI in XML development. The potential benefits and the possible pitfalls will be highlighted, providing a balanced view of the subject.
We will explore the capabilities of AI in understanding XML markup languages and autonomously creating structured XML content. Additionally, we will examine the capacity of AI to enrich plain text with appropriate XML markup. Practical examples and methodological guidelines will be provided to elucidate how AI can be effectively prompted to interpret and generate accurate XML markup.
Further emphasis will be placed on the role of AI in developing XSLT, or schemas such as XSD and Schematron. We will address the techniques and strategies adopted to create prompts for generating code, explaining code, or refactoring the code, and the results achieved.
The discussion will extend to how AI can be used to transform XML content. In particular, the focus will be on the use of AI XPath extension functions in XSLT, Schematron, Schematron Quick Fixes, or for XML content refactoring.
The presentation aims to deliver a comprehensive overview of AI usage in XML development, providing attendees with the necessary knowledge to make informed decisions. Whether you’re at the early stages of adopting AI or considering integrating it in advanced XML development, this presentation will cover all levels of expertise.
By highlighting the potential advantages and challenges of integrating AI with XML development tools and languages, the presentation seeks to inspire thoughtful conversation around the future of XML development. We’ll not only delve into the technical aspects of AI-powered XML development but also discuss practical implications and possible future directions.
AI 101: An Introduction to the Basics and Impact of Artificial IntelligenceIndexBug
Imagine a world where machines not only perform tasks but also learn, adapt, and make decisions. This is the promise of Artificial Intelligence (AI), a technology that's not just enhancing our lives but revolutionizing entire industries.
Let's Integrate MuleSoft RPA, COMPOSER, APM with AWS IDP along with Slackshyamraj55
Discover the seamless integration of RPA (Robotic Process Automation), COMPOSER, and APM with AWS IDP enhanced with Slack notifications. Explore how these technologies converge to streamline workflows, optimize performance, and ensure secure access, all while leveraging the power of AWS IDP and real-time communication via Slack notifications.
Cosa hanno in comune un mattoncino Lego e la backdoor XZ?Speck&Tech
ABSTRACT: A prima vista, un mattoncino Lego e la backdoor XZ potrebbero avere in comune il fatto di essere entrambi blocchi di costruzione, o dipendenze di progetti creativi e software. La realtà è che un mattoncino Lego e il caso della backdoor XZ hanno molto di più di tutto ciò in comune.
Partecipate alla presentazione per immergervi in una storia di interoperabilità, standard e formati aperti, per poi discutere del ruolo importante che i contributori hanno in una comunità open source sostenibile.
BIO: Sostenitrice del software libero e dei formati standard e aperti. È stata un membro attivo dei progetti Fedora e openSUSE e ha co-fondato l'Associazione LibreItalia dove è stata coinvolta in diversi eventi, migrazioni e formazione relativi a LibreOffice. In precedenza ha lavorato a migrazioni e corsi di formazione su LibreOffice per diverse amministrazioni pubbliche e privati. Da gennaio 2020 lavora in SUSE come Software Release Engineer per Uyuni e SUSE Manager e quando non segue la sua passione per i computer e per Geeko coltiva la sua curiosità per l'astronomia (da cui deriva il suo nickname deneb_alpha).
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
TrustArc Webinar - 2024 Global Privacy SurveyTrustArc
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
Driving Business Innovation: Latest Generative AI Advancements & Success StorySafe Software
Are you ready to revolutionize how you handle data? Join us for a webinar where we’ll bring you up to speed with the latest advancements in Generative AI technology and discover how leveraging FME with tools from giants like Google Gemini, Amazon, and Microsoft OpenAI can supercharge your workflow efficiency.
During the hour, we’ll take you through:
Guest Speaker Segment with Hannah Barrington: Dive into the world of dynamic real estate marketing with Hannah, the Marketing Manager at Workspace Group. Hear firsthand how their team generates engaging descriptions for thousands of office units by integrating diverse data sources—from PDF floorplans to web pages—using FME transformers, like OpenAIVisionConnector and AnthropicVisionConnector. This use case will show you how GenAI can streamline content creation for marketing across the board.
Ollama Use Case: Learn how Scenario Specialist Dmitri Bagh has utilized Ollama within FME to input data, create custom models, and enhance security protocols. This segment will include demos to illustrate the full capabilities of FME in AI-driven processes.
Custom AI Models: Discover how to leverage FME to build personalized AI models using your data. Whether it’s populating a model with local data for added security or integrating public AI tools, find out how FME facilitates a versatile and secure approach to AI.
We’ll wrap up with a live Q&A session where you can engage with our experts on your specific use cases, and learn more about optimizing your data workflows with AI.
This webinar is ideal for professionals seeking to harness the power of AI within their data management systems while ensuring high levels of customization and security. Whether you're a novice or an expert, gain actionable insights and strategies to elevate your data processes. Join us to see how FME and AI can revolutionize how you work with data!
Communications Mining Series - Zero to Hero - Session 1DianaGray10
This session provides introduction to UiPath Communication Mining, importance and platform overview. You will acquire a good understand of the phases in Communication Mining as we go over the platform with you. Topics covered:
• Communication Mining Overview
• Why is it important?
• How can it help today’s business and the benefits
• Phases in Communication Mining
• Demo on Platform overview
• Q/A
Full-RAG: A modern architecture for hyper-personalizationZilliz
Mike Del Balso, CEO & Co-Founder at Tecton, presents "Full RAG," a novel approach to AI recommendation systems, aiming to push beyond the limitations of traditional models through a deep integration of contextual insights and real-time data, leveraging the Retrieval-Augmented Generation architecture. This talk will outline Full RAG's potential to significantly enhance personalization, address engineering challenges such as data management and model training, and introduce data enrichment with reranking as a key solution. Attendees will gain crucial insights into the importance of hyperpersonalization in AI, the capabilities of Full RAG for advanced personalization, and strategies for managing complex data integrations for deploying cutting-edge AI solutions.